This material assumes that you have programmed before. This first lecture provides a quick introduction to programming in Python for those who either haven't used Python before or need a quick refresher.

Let's start with a hypothetical problem we want to solve. We are interested in understanding the relationship between the weather and the number of mosquitos occuring in a particular year so that we can plan mosquito control measures accordingly. Since we want to apply these mosquito control measures at a number of different sites we need to understand both the relationship at a particular site and whether or not it is consistent across sites. The data we have to address this problem comes from the local government and are stored in tables in comma-separated values (CSV) files. Each file holds the data for a single location, each row holds the information for a single year at that location, and the columns hold the data on both mosquito numbers and the average temperature and rainfall from the beginning of mosquito breeding season. The first few rows of our first file look like:



  • Conduct variable assignment, looping, and conditionals in Python
  • Use an external Python library
  • Read tabular data from a file
  • Subset and perform analysis on data
  • Display simple graphs

Loading Data

In order to load the data, we need to import a library called Pandas that knows how to operate on tables of data.

In [1]:
import pandas

We can now use Pandas to read our data file.

In [2]:
year temperature rainfall mosquitos
0 2001 87 222 198
1 2002 72 103 105
2 2003 77 176 166
3 2004 89 236 210
4 2005 88 283 242
5 2006 89 151 147
6 2007 71 121 117
7 2008 88 267 232
8 2009 85 211 191
9 2010 75 101 106

The read_csv() function belongs to the pandas library. In order to run it we need to tell Python that it is part of pandas and we do this using the dot notation, which is used everywhere in Python to refer to parts of larger things.

When we are finished typing and press Shift+Enter, the notebook runs our command and shows us its output. In this case, the output is the data we just loaded.

Our call to pandas.read_csv() read data into memory, but didn't save it anywhere. To do that, we need to assign the array to a variable. In Python we use = to assign a new value to a variable like this:

In [3]:
data = pandas.read_csv('mosquito_data_A1.csv')

This statement doesn't produce any output because assignment doesn't display anything. If we want to check that our data has been loaded, we can print the variable's value:

In [4]:
print data
   year  temperature  rainfall  mosquitos
0  2001           87       222        198
1  2002           72       103        105
2  2003           77       176        166
3  2004           89       236        210
4  2005           88       283        242
5  2006           89       151        147
6  2007           71       121        117
7  2008           88       267        232
8  2009           85       211        191
9  2010           75       101        106

print data tells Python to display the text. Alternatively we could just include data as the last value in a code cell:

In [5]:
year temperature rainfall mosquitos
0 2001 87 222 198
1 2002 72 103 105
2 2003 77 176 166
3 2004 89 236 210
4 2005 88 283 242
5 2006 89 151 147
6 2007 71 121 117
7 2008 88 267 232
8 2009 85 211 191
9 2010 75 101 106

This tells the IPython Notebook to display the data object, which is why we see a pretty formated table.

Manipulating data

Once we have imported the data we can start doing things with it. First, let's ask what type of thing data refers to:

In [6]:
print type(data)
<class 'pandas.core.frame.DataFrame'>

The data is stored in a data structure called a DataFrame. There are other kinds of data structures that are also commonly used in scientific computing including Numpy arrays, and Numpy matrices, which can be used for doing linear algebra.

We can select an individual column of data using its name:

In [7]:
print data['year']
0    2001
1    2002
2    2003
3    2004
4    2005
5    2006
6    2007
7    2008
8    2009
9    2010
Name: year, dtype: int64

Or we can select several columns of data at once:

In [8]:
print data[['rainfall', 'temperature']]
   rainfall  temperature
0       222           87
1       103           72
2       176           77
3       236           89
4       283           88
5       151           89
6       121           71
7       267           88
8       211           85
9       101           75

We can also select subsets of rows using slicing. Say we just want the first two rows of data:

In [9]:
print data[0:2]
   year  temperature  rainfall  mosquitos
0  2001           87       222        198
1  2002           72       103        105

There are a couple of important things to note here. First, Python indexing starts at zero. In contrast, programming languages like R and MATLAB start counting at 1, because that's what human beings have done for thousands of years. Languages in the C family (including C++, Java, Perl, and Python) count from 0 because that's simpler for computers to do. This means that if we have 5 things in Python they are numbered 0, 1, 2, 3, 4, and the first row in a data frame is always row 0.

The other thing to note is that the subset of rows starts at the first value and goes up to, but does not include, the second value. Again, the up-to-but-not-including takes a bit of getting used to, but the rule is that the difference between the upper and lower bounds is the number of values in the slice.

One thing that we can't do with this syntax is directly ask for the data from a single row:

In [10]:
KeyError                                  Traceback (most recent call last)
<ipython-input-10-c805864c0d75> in <module>()
----> 1 data[1]

/usr/lib/python2.7/dist-packages/pandas/core/frame.pyc in __getitem__(self, key)
   2001             # get column
   2002             if self.columns.is_unique:
-> 2003                 return self._get_item_cache(key)
   2005             # duplicate columns

/usr/lib/python2.7/dist-packages/pandas/core/generic.pyc in _get_item_cache(self, item)
    665             return cache[item]
    666         except Exception:
--> 667             values = self._data.get(item)
    668             res = self._box_item_values(item, values)
    669             cache[item] = res

/usr/lib/python2.7/dist-packages/pandas/core/internals.pyc in get(self, item)
   1653     def get(self, item):
   1654         if self.items.is_unique:
-> 1655             _, block = self._find_block(item)
   1656             return block.get(item)
   1657         else:

/usr/lib/python2.7/dist-packages/pandas/core/internals.pyc in _find_block(self, item)
   1934     def _find_block(self, item):
-> 1935         self._check_have(item)
   1936         for i, block in enumerate(self.blocks):
   1937             if item in block:

/usr/lib/python2.7/dist-packages/pandas/core/internals.pyc in _check_have(self, item)
   1940     def _check_have(self, item):
   1941         if item not in self.items:
-> 1942             raise KeyError('no item named %s' % com.pprint_thing(item))
   1944     def reindex_axis(self, new_axis, method=None, axis=0, copy=True):

KeyError: u'no item named 1'

This is because there are several things that we could mean by data[1] so if we want a single row we can either take a slice that returns a single row:

In [11]:
print data[1:2]
   year  temperature  rainfall  mosquitos
1  2002           72       103        105

or use the .iloc method, which stands for "integer location" since we are looking up the row based on its integer index.

In [12]:
print data.iloc[1]
year           2002
temperature      72
rainfall        103
mosquitos       105
Name: 1, dtype: int64

We can also use this same syntax for getting larger subsets of rows:

In [13]:
print data.iloc[1:3]
   year  temperature  rainfall  mosquitos
1  2002           72       103        105
2  2003           77       176        166

We can also subset the data based on the value of other rows:

In [14]:
print data['temperature'][data['year'] > 2005]
5    89
6    71
7    88
8    85
9    75
Name: temperature, dtype: int64

Data frames also know how to perform common mathematical operations on their values. If we want to find the average value for each variable, we can just ask the data frame for its mean values

In [15]:
print data.mean()
year           2005.5
temperature      82.1
rainfall        187.1
mosquitos       171.4
dtype: float64

Data frames have lots of useful methods:

In [16]:
print data.max()
year           2010
temperature      89
rainfall        283
mosquitos       242
dtype: int64
In [17]:
print data['temperature'].min()
In [18]:
print data['mosquitos'][1:3].std()


Import the data from mosquito_data_A2.csv, create a new variable that holds a data frame with only the weather data, and print the means and standard deviations for the weather variables.


Once we have some data we often want to be able to loop over it to perform the same operation repeatedly. A for loop in Python takes the general form

for item in list:

So if we want to loop over the temperatures and print out there values in degrees Celcius (instead of Farenheit) we can use:

In [19]:
temps = data['temperature']
for temp_in_f in temps:
    temp_in_c = (temp_in_f - 32) * 5 / 9.0
    print temp_in_c

That looks good, but why did we use 9.0 instead of 9? The reason is that computers store integers and numbers with decimals as different types: integers and floating point numbers (or floats). Addition, subtraction and multiplication work on both as we'd expect, but division works differently. If we divide one integer by another, we get the quotient without the remainder:

In [20]:
print '10/3 is:', 10 / 3
10/3 is: 3

If either part of the division is a float, on the other hand, the computer creates a floating-point answer:

In [21]:
print '10/3.0 is:', 10 / 3.0
10/3.0 is: 3.33333333333

The computer does this for historical reasons: integer operations were much faster on early machines, and this behavior is actually useful in a lot of situations. However, it's still confusing, so Python 3 produces a floating-point answer when dividing integers if it needs to. We're still using Python 2.7 in this class, so if we want 5/9 to give us the right answer, we have to write it as 5.0/9, 5/9.0, or some other variation.


The other standard thing we need to know how to do in Python is conditionals, or if/then/else statements. In Python the basic syntax is:

if condition:

So if we want to loop over the temperatures and print out only those temperatures that are greater than 80 degrees we would use:

In [22]:
temp = data['temperature'][0]
if temp > 80:
    print "The temperature is greater than 80"
The temperature is greater than 80

We can also use == for equality, <= for less than or equal to, >= for greater than or equal to, and != for not equal to.

Additional conditions can be handled using elif and else:

In [23]:
temp = data['temperature'][0]
if temp < 87:
    print "The temperature is < 87"
elif temp > 87:
    print "The temperature is > 87"
    print " The temperature is equal to 87"
 The temperature is equal to 87


Import the data from mosquito_data_A2.csv, determine the mean temperate, and loop over the temperature values. For each value print out whether it is greater than the mean, less than the mean, or equal to the mean.


The mathematician Richard Hamming once said, "The purpose of computing is insight, not numbers," and the best way to develop insight is often to visualize data. The main plotting library in Python is matplotlib. To get started, let's tell the IPython Notebook that we want our plots displayed inline, rather than in a separate viewing window:

In [24]:
%matplotlib inline

The % at the start of the line signals that this is a command for the notebook, rather than a statement in Python. Next, we will import the pyplot module from matplotlib, but since pyplot is a fairly long name to type repeatedly let's give it an alias.

In [25]:
from matplotlib import pyplot as plt

This import statement shows two new things. First, we can import part of a library by using the from library import submodule syntax. Second, we can use a different name to refer to the imported library by using as newname.

Now, let's make a simple plot showing how the number of mosquitos varies over time. We'll use the site you've been doing exercises with since it has a longer time-series.

In [26]:
data = pandas.read_csv('mosquito_data_A2.csv')
plt.plot(data['year'], data['mosquitos'])
[<matplotlib.lines.Line2D at 0x4a88590>]

More complicated plots can be created by adding a little additional information. Let's say we want to look at how the different weather variables vary over time.

In [27]:
plt.figure(figsize=(10.0, 3.0))

plt.subplot(1, 2, 1)
plt.plot(data['year'], data['temperature'], 'ro-')

plt.subplot(1, 2, 2)
plt.plot(data['year'], data['rainfall'], 'bs-')
plt.ylabel('Rain Fall')


Using the data in mosquito_data_A2.csv plot the relationship between the number of mosquitos and temperature and the number of mosquitos and rainfall.

Key Points

  • Import a library into a program using import libraryname.
  • Use the pandas library to work with data tables in Python.
  • Use variable = value to assign a value to a variable.
  • Use print something to display the value of something.
  • Use dataframe['columnname'] to select a column of data.
  • Use dataframe[start_row:stop_row] to select rows from a data frame.
  • Indices start at 0, not 1.
  • Use dataframe.mean(), dataframe.max(), and dataframe.min() to calculate simple statistics.
  • Use for x in list: to loop over values
  • Use if condition: to make conditional decisions
  • Use the pyplot library from matplotlib for creating simple visualizations.

Next steps

With the requisite Python background out of the way, now we're ready to dig in to analyzing our data, and along the way learn how to write better code, more efficiently, that is more likely to be correct.